A conventional spectrum allocation policy suffers from a shortage of spectrum. The shortage limits ability to introduce new wireless products, services, and applications such as pervasive broadband Internet access. The spectrum shortage limits also ability to make current systems (for example cellular telephony) more common and less expensive, to increase the data rates and ranges of existing products like WiFi, and the shortage limits even the ability to provide public safety authorities with the communications systems they need to do their jobs. The radio frequency spectrum bands are mostly allocated to existing licensed users. Thus, there does not seem to be much room for new wireless services. However, spectrum measurements conducted around the world reveal that many bands are used only part of the time and/or locally. With a proper spectrum sharing mechanisms and policies more efficient spectrum use could be achieved since the system needing more bandwidth could access also other bands than its own licensed band to achieve higher capacity.
Spectrum data gathering and analysis have been published in many opportunistic spectrum use documents in recent years. In them several parameters have been proposed to be gathered about the radio environment [M. McHenry, D. McCloskey, G. Minden, and D. Roberson, “Multi-band multi-location spectrum occupancy measurements” in Proc. ISART, March 2006] and [J. Lehtomäki, R. Vuohtoniemi, K. Umebayashi, and J. P. Mäkelä, “Energy detection based estimation of channel occupancy rate with adaptive noise estimation”, IEICE Transactions on Communications, vol. E95-B, No. 4, pp. 1076-1084, April 2012].
However, most of the publications do not concentrate deeply on parameter selection. In some publications adaptive parameter settings have been investigated regarding the threshold, resolution bandwidth and sweep time to increase the accuracy in the occupancy measurements in ISM bands [D. Denkovski, M. Pavloski, V. Atanasovski, and L. Gavrilovska, “Parameter settings for 2.4 GHz ISM spectrum measurements” in Proc. ISABEL, November 2010].
Spectrum sharing models using both short term and long term information in spectrum sharing have been proposed. Traffic prediction may be performed for different traffic models, e.g. assuming exponentially distributed idle periods or using binomial distributed call arrival and gamma distributed call holding times. In publication of X. Li and S. A. Zekavat, “Traffic pattern prediction and performance investigation for cognitive radio systems” in Proc. WCNC, pp. 894-899, March-April 2008, it is described a 24-hour period prediction case for cognitive radio systems.
There are still problems to be solved. One problem is how reliable spectrum awareness information could be obtained for deciding which channels could be used for augmenting the current system bandwidth.
A second problem is what kind of information is needed about the candidate frequency channels to be able to decide whether they are good candidates for a transmission channel of the application in mind.
A third problem is what kind of decision-making is needed for channel selection when multiple parameters such as bandwidth and delay requirements of the application/service, spectrum occupancy and idle time statistics from different frequency bands are required.
Commercial significance of the above-mentioned problems lay in the fact that spectrum is an expensive and limited resource. Thus, an efficient spectrum analysing and sharing approach enabling high spectrum occupancy would be very valuable for the network operators. Also end users would like the method allowing more and better services to be served with the same price.